Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis
One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1036562/full |
| _version_ | 1811162660306157568 |
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| author | Lisha Mou Lisha Mou Zuhui Pu Yongxiang Luo Ryan Quan Yunhu So Hui Jiang |
| author_facet | Lisha Mou Lisha Mou Zuhui Pu Yongxiang Luo Ryan Quan Yunhu So Hui Jiang |
| author_sort | Lisha Mou |
| collection | DOAJ |
| description | One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients. |
| first_indexed | 2024-04-10T06:32:50Z |
| format | Article |
| id | doaj.art-58bba5cf989b4ebbbe58acca0d5807de |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-10T06:32:50Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-58bba5cf989b4ebbbe58acca0d5807de2023-03-01T05:16:33ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-03-011410.3389/fimmu.2023.10365621036562Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysisLisha Mou0Lisha Mou1Zuhui Pu2Yongxiang Luo3Ryan Quan4Yunhu So5Hui Jiang6Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, ChinaMetaLife Center, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, ChinaImaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, ChinaDepartment of General Surgery, The First People's Hospital of Qinzhou/The Tenth Affiliated Hospital of Guangxi Medical University, Qinzhou, Guangxi, ChinaMetaLife Center, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, ChinaMetaLife Center, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, ChinaDepartment of General Surgery, The First People's Hospital of Qinzhou/The Tenth Affiliated Hospital of Guangxi Medical University, Qinzhou, Guangxi, ChinaOne of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1036562/fullhepatocellular carcinomascRNA-seqlipid metabolismprediction modelimmune microenvironmentTMB |
| spellingShingle | Lisha Mou Lisha Mou Zuhui Pu Yongxiang Luo Ryan Quan Yunhu So Hui Jiang Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis Frontiers in Immunology hepatocellular carcinoma scRNA-seq lipid metabolism prediction model immune microenvironment TMB |
| title | Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis |
| title_full | Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis |
| title_fullStr | Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis |
| title_full_unstemmed | Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis |
| title_short | Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis |
| title_sort | construction of a lipid metabolism related risk model for hepatocellular carcinoma by single cell and machine learning analysis |
| topic | hepatocellular carcinoma scRNA-seq lipid metabolism prediction model immune microenvironment TMB |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1036562/full |
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